Change-point detection (CPD), which detects abrupt changes in the data distribution, is
recognized as one of the most significant tasks in time series analysis. Despite the extensive …

We address the problem of online change detection in multivariate datastreams, and we
introduce QuantTree Exponentially Weighted Moving Average (QT-EWMA), a nonparametric …

We address the problem of detecting distribution changes in a novel batch-wise and
multimodal setup. This setup is characterized by a stationary condition where batches are …

We introduce Class Distribution Monitoring (CDM), an effective concept-drift detection
scheme that monitors the class-conditional distributions of a datastream. In particular, our …

As the demand for artificial intelligence (AI) and machine learning (ML) technologies
continues to surge across industries, it has become increasingly vital for professionals to …

Recent research yielded a wide array of drift detectors. However, in order to achieve
remarkable performance, the true class labels must be available during the drift detection …

We present Kernel-QuantTree Exponentially Weighted Moving Average (KQT-EWMA), a non-
parametric change-detection algorithm that combines the Kernel-QuantTree (KQT) …

This chapter highlights best practices and pitfalls in the development of ML systems.
Following the best practices and avoiding the pitfalls presented should increase the …

This chapter discusses drift detection in the context of data observed in a time-ordered
sequence, as opposed to the static datasets in Chaps. 7 and 8. It covers concepts including …

This chapter discusses the concepts of populations and samples of observational units and
the idea of drift detection as being an inference that drift has occurred in a population …